MUTHAYAMMAL

ENGINEERING COLLEGE

DEPARTMENT OF BIOMEDICAL

ENGINEERING

20/12/2019

ARTIFICAL INTELLIGENCE

Ms.A.ANJALI,

AP/BME.

AI Definitions

•The study of how to make programs/computers do

things that people do better

•The study of how to make computers solve problems

which require knowledge and intelligence

• The exciting new effort to make computers think …

machines with minds

•The automation of activities that we associate with

human thinking (e.g., decision-making, learning…)

•The art of creating machines that perform functions

that require intelligence when performed by people

•The study of mental faculties through the use of

computational models

•A field of study that seeks to explain and emulate

intelligent behavior in terms of computational

processes

•The branch of computer science that is concerned

with the automation of intelligent behavior

Thinking

machines or

machine

intelligence

Studying

cognitive

faculties

Problem

Solving and

So What Is AI?

•AI as a field of study

–Computer Science

–Cognitive Science

–Psychology

–Philosophy

–Linguistics

–Neuroscience

•AI is part science, part engineering

•AI often must study other domains in order to implement systems

–e.g., medicine and medical practices for a medical diagnostic system,

engineering and chemistry to monitor a chemical processing plant

•AI is a belief that the brain is a form of biological computer and

that the mind is computational

•AI has had a concrete impact on society but unlike other areas of

CS, the impact is often

–felt only tangentially (that is, people are not aware that system X has AI)

–felt years after the initial investment in the technology

What is Intelligence?

• Is there a “holistic” definition for intelligence?

•Here are some definitions:

–the ability to comprehend; to understand and profit from experience

–a general mental capability that involves the ability to reason, plan, solve

problems, think abstractly, comprehend ideas and language, and learn

–is effectively perceiving, interpreting and responding to the environment

•None of these tells us what intelligence is, so instead, maybe we

can enumerate a list of elements that an intelligence must be able

to perform:

–perceive, reason and infer, solve problems, learn and adapt, apply

common sense, apply analogy, recall, apply intuition, reach emotional

states, achieve self-awareness

•Which of these are necessary for intelligence? Which are

sufficient?

•Artificial Intelligence –should we define this in terms of human

intelligence?

–does AI have to really be intelligent?

–what is the difference between being intelligent and demonstrating

intelligent behavior?

Physical Symbol System Hypothesis

•A physical symbol system (PSS)

–consists of symbols (patterns) which are combinable into

expressions

–there are processes which operate on these symbols to create

new symbols and expressions

•consider for instance English as a physical symbol system

•symbols are the alphabet

•expressions are words and sentences

•the processes are the English grammar and parsers and dictionaries

•The PSS Hypothesis states that a PSS has the necessary

and sufficient means for intelligent action

–a computer is a PSS

•if the PSS Hypothesis is true, then it should be possible to program a

computer to produce intelligent actions

•this is the (or a) goal of AI

•if the PSS Hypothesis is not true, what are the consequences for AI?

The Turing Test

•1950 –Alan Turing

devised a test for

intelligence called the

Imitation Game

–Ask questions of two

entities, receive answers

from both

– If you can’t tell which of the

entities is human and which

is a computer program, then

you are fooled and we

should therefore consider the

computer to be intelligent

Questions

Answers Answers

Which is the person?

Which is the computer?

Solving the Turing Test: Eliza

•Created by Joseph Weizenbaum

•This system would act like a Rogerian psychoanalyst by

holding a dialog with a person

–the dialog would be consist of the doctor (Eliza) asking

questions, the human responding, and the doctor using the

response to ask another question

•Weizenbaum wrote the program so that it would

generate an English response/question based on a group

of patterns

–If the user sentence matched a pattern, this pattern would be

used to generate the next sentence/question

–A more recent, and much superior version is called A.L.I.C.E,

see http://alicebot.blogspot.com/

• click on “chat with A.L.I.C.E.”

Example

Eliza

Dialog

–H: Men are all alike

–E: In what way?

– H: They’re always bugging us about something or

another.

–E: Can you be more specific?

–H: Well, my boyfriend made me come here.

–E: Your boyfriend made you come here?

– H: He says I’m depressed much of the time.

–E: I am sorry to hear you are depressed.

– H: It’s true. I am unhappy.

–E: Do you think coming here will help you not to be

unhappy?

–H: I need some help, that much seems certain.

–E: What would it mean to you if you got some help?

–H: Perhaps I could learn to get along with my mother.

–E: Tell me more about your family

Eliza

Rules

(?* ?x) hello (?* ?y) 

(How do you do. Please state your problem.)

(?* ?x) I want (?* ?y) 

(What would it mean if you got ?y)

(Why do you want ?y)

(Suppose you got ?y soon)

(?* ?x) if (?* ?y) 

(Do you really think its likely that ?y)

(Do you wish that ?y)

(What do you think about ?y) (Really-- if ?y)

(?* ?x) no (?* ?y) (Why not?)

(You are being a bit negative)

(Are you saying "NO" just to be negative?)

(?* ?x) I was (?* ?y) (Were you really?)

(Perhaps I already knew you were ?y)

(Why do you tell me you were ?y now?)

(?* ?x) I feel (?* ?y) (Do you often feel ?y ?)

(?* ?x) I felt (?* ?y) 

(What other feelings do you have?)

Eliza also has a

rule to swap

‘I’ and ‘my’

to ‘you’ and ‘your’

Eliza Pattern Syntax

pat var match any one expression to a variable

constant or to a constant (see below)

segment-pat match against a sequence

single-pat match against one expression

(pat . pat) match the first and the rest of a list

single-pat 

(?is var predicate) test predicate on one expression

(?or pat1 pat2 …) match on any of the patterns

(?and pat1 pat2 …) match on every of the expressions

(?not pat) match if expression does not match

segment-pat 

((?* var) …) match on zero or more expressions

((?+ var) …) match on one or more expressions

((?? var) …) match zero or one expression

((?if expr) …) test if expression is true

var ?chars variables of the form ?name

constant atom symbols, numbers, chars

ALICE Demo

Jerry: my name is jerry.

ALICE: Hey jerry.

Jerry: do you know Turing?

ALICE: A brilliant and tragic figure in the history of computer science.

Jerry: who else do you know?

ALICE: Hugh Loebner, Robby Garner, and Steven Spielberg

Jerry: which film do you like?

ALICE: My favorite movie is Teknolust. Have you seen it?

Jerry: no.

ALICE: I would recommend you check it out.

Jerry: what is it about?

ALICE: Science fiction.

Jerry: do you remember my name?

ALICE: Your name is jerry, seeker.

Notice unlike Eliza, ALICE has a memory, can handle references

(have you seen it? no. I would recommend it.)

ALICE vs. Eliza

ALICE talking to ELIZA

ELIZA: I am ELIZA.

ALICE: I am very pleased to meet you ELIZA.

ELIZA: How can I help you?

ALICE: Just keep talking to me as long as you can.

ELIZA: Do you wish to as long as me?

ALICE: OK, I will as long as you.

ELIZA: Do you wish to will as long as me?

ALICE: OK, I will will as long as you.

ELIZA: Do you wish to will will as long as me?

ALICE: OK, I will will will as long as you.

ELIZA: Do you wish to will will will as long as me?

ALICE: OK, I will will will will as long as you.

ELIZA: Do you wish to will will will will as long as me?

ALICE: OK, I will will will will will as long as you.

ELIZA: Do you wish to will will will will will as long as me?

…

Eliza gets stuck on the phrase “I will” and then ALICE gets stuck

on the same phrase

How Useful is the Turing Test?

•With Eliza or Alice like rules, we can eventually solve the

Turing Test –it just takes writing enough rules

•Does the system understand what it is responding to?

–No, neither Eliza nor Alice understand the text, its just that Alice

has better, more in depth and wider ranging rules

•However, we could build a representation that

models some real-world domain and knowledge

base

–The system can fill in information from the conversation

•this is sort of like a database, or an object with data attributes to be filled in

•we can use a variety of AI representations like scripts, frames, semantic

networks

–Questions can be responded to by looking up the stored data

– In this way, the system is responding, not based merely on “canned”

knowledge, but on knowledge that it has “learned”

•Does this imply that the system knows what it is discussing?

–What does it mean to know something?

Table-Lookup vs. Reasoning

•Consider two approaches to programming a Tic-Tac-Toe player

–Solution 1: a pre-enumerated list of best moves given the board

configuration

–Solution 2: rules (or a heuristic function) that evaluate a board

configuration, and using these to select the next best move

•Solution 1 is similar to how Eliza works

–This is not practical for most types of problems

–Consider solving the game of chess in this way, or trying to come up with

all of the responses that a Turing Test program might face

•Solution 2 will reason out the best move

–Given the board configuration, it will analyze each available move and

determine which is the best

– Such a player might even be able to “explain” why it chose the move it did

•We can (potentially) build a program that can pass the Turing

Test using table-lookup even though it would be a large

undertaking

•Could we build a program that can pass the Turing Test using

reasoning?

–Even if we can, does this necessarily mean that the system is intelligent?

Slot Filling

•Roger Schank created the

Script representation

–the script describes typical

sequences of actions and

actors for some real-world

situation

–a story (newspaper article) is

parsed and slots are filled in

–SAM (Script Applier

Mechanism) uses the filled in

script to answer questions

•The Script provides the

knowledge needed to

respond like a human and

thus solve the Turing Test

Schank’s Restaurant script

The Chinese Room Problem

•From John Searle, Philosopher, in an attempt to

demonstrate that computers cannot be intelligent

–The room consists of you, a book, a storage area (optional),

and a mechanism for moving information to and from the

room to the outside

•a Chinese speaking individual provides a question for you in writing

•you are able to find a matching set of symbols in the book (and

storage) and write a response, also in Chinese

Question (Chinese)

Book of Chinese Symbols

Answer

(Chinese)

Storage You

Chinese Room:

An Analogy for a Computer

User Input I/O pathway (bus) Output

Memory Program/Data

(Script) CPU (SAM)

Note: Searle’s original Chinese Room actually was based on a

Script that was implemented in Chinese, our version is just a

variation on the same theme

Searle’s Question

•You were able to solve the problem of communicating with the

person/user and thus you/the room passes the Turing Test

•But did you understand the Chinese messages being

communicated?

–since you do not speak Chinese, you did not understand the symbols in the

question, the answer, or the storage

–can we say that you actually used any intelligence?

•By analogy, since you did not understand the symbols that you

interacted with, neither does the computer understand the

symbols that it interacts with (input, output, program code, data)

•Searle concludes that the computer is not intelligent, it has no

“semantics,” but instead is merely a symbol manipulating device

–the computer operates solely on syntax, not semantics

•He defines to categories of AI:

–strong AI –the pursuit of machine intelligence

–weak AI –the pursuit of machines solving problems in an intelligent way

But Computers Solve Problems

•We can clearly see that computers solve problems in a

seemingly intelligent way

–Where is the intelligence coming from?

• There are numerous responses to Searle’s argument

– The System’s Response:

•the hardware by itself is not intelligent, but a combination of the

hardware, software and storage is intelligent

•in a similar vein, we might say that a human brain that has had no

opportunity to learn anything cannot be intelligent, it is just the

hardware

–The Robot Response:

•a computer is void of senses and therefore symbols are meaningless to

it, but a robot with sensors can tie its symbols to its senses and thus

understand symbols

–The Brain Simulator Response:

•if we program a computer to mimic the brain (e.g., with a neural

network) then the computer will have the same ability to understand as

a human brain

Brain vs. Computer

•In AI, we compare the brain (or the mind) and the

computer

–Our hope: the brain is a form of computer

–Our goal: we can create computer intelligence through

programming just as people become intelligent by learning

But we see that the computer

is not like the brain

The computer performs tasks

without understanding what

its doing

Does the brain understand

what its doing when it solves

problems?

Symbol Grounding

•One problem with the computer is that it works strictly

syntactically

–Op code: 10011101 translates into a set of microcode

instructions such as: move IR16..31 to MAR, signal memory

read, move MBR to AC

–There is no understanding

•x = y + z; is meaningless to the computer

– the computer doesn’t understand addition, it just knows that a certain op

code means to move values to the adder and move the result elsewhere

•do you know what addition means?

–if so, how do you proscribe meaning to +

–how is this symbol grounded in your brain?

–can computers similarly achieve this?

– Recall Schank’s Restaurant script

• does the computer know what the symbols “waiter” or “PTRANS”

represent? or does it merely have code that tells the computer what to

do when it comes across certain words in the story, or how to respond

to a given question?

Two AI Assumptions

•We can understand and model cognition without

understanding the underlying mechanism

–That is, it is the model of cognition that is important not the

physical mechanism that implements it

–If this is true, then we should be able to create cognition (mind)

out of a computer or a brain or even other entities that can

compute such as a mechanical device

•This is the assumption made by symbolic AI researchers

•Cognition will emerge from the proper mechanism

–That is, the right device, fed with the right inputs, can learn and

perform the problem solving that we, as observers, call

intelligence

–Cognition will arise as the result (or side effect) of the hardware

•This is the assumption made by connectionist AI researchers

•Notice that while the two assumptions differ, neither is

necessarily mutually exclusive and both support the idea

that cognition is computational

Problems with Symbolic AI Approaches

•Scalability

–It can take dozens or more man-years to create a useful

systems

–It is often the case that systems perform well up to a certain

threshold of knowledge (approx. 10,000 rules), after which

performance (accuracy and efficiency) degrade

•Brittleness

–Most symbolic AI systems are programmed to solve a specific

problem, move away from that domain area and the system’s

accuracy drops rapidly rather than achieving a graceful

degradation

•this is often attributed to lack of common sense, but in truth, it is a lack

of any knowledge outside of the domain area

–No or little capacity to learn, so performance (accuracy) is

static

•Lack of real-time performance

Problems with Connectionist AI Approaches

• No “memory” or sense of temporality

–The first problem can be solved to some extent

–The second problem arises because of a fixed sized input but

leads to poor performance in areas like speech recognition

•Learning is problematic

–Learning times can greatly vary

–Overtraining leads to a system that only performs well on the

training set and undertraining leads to a system that has not

generalized

•No explicit knowledge-base

–So there is no way to tell what a system truly knows or how it

knows something

•No capacity to explain its output

–Explanation is often useful in an AI system so that the user can

trust the system’s answer

So What Does AI Do?

•Most AI research has fallen into one of two categories

–Select a specific problem to solve

•study the problem (perhaps how humans solve it)

•come up with the proper representation for any knowledge needed to

solve the problem

•acquire and codify that knowledge

•build a problem solving system

–Select a category of problem or cognitive activity (e.g.,

learning, natural language understanding)

•theorize a way to solve the given problem

•build systems based on the model behind your theory as experiments

•modify as needed

•Both approaches require

–one or more representational forms for the knowledge

–some way to select proper knowledge, that is, search

What is Search?

•We define the state of the problem being solved as the

values of the active variables

–this will include any partial solutions, previous conclusions, user

answers to questions, etc

•while humans are often

able to make intuitive

leaps, or recall solutions

with little thought, the

computer must search

through various

combinations to find a

solution

•To the right is a search

space for a tic-tac-toe

game

Search Spaces and Types of Search

•The search space consists of all possible states of the

problem as it is being solved

–A search space is often viewed as a tree and can very well

consist of an exponential number of nodes making the search

process intractable

–Search spaces might be pre-enumerated or generated during

the search process

–Some search algorithms may search the entire space until a

solution is found, others will only search parts of the space,

possibly selecting where to search through a heuristic

•Search spaces include

–Game trees like the tic-tac-toe game

–Decision trees (see next slides)

–Combinations of rules to select in a production system

–Networks of various forms (see next slides)

–Other types of spaces

Search Algorithms and Representations

•Breadth-first

•Depth-first

•Best-first (Heuristic Search)

•A*

•Hill Climbing

•Limiting the number of

Plies

•Minimax

•Alpha-Beta Pruning

•Adding Constraints

•Genetic Algorithms

•Forward vs Backward

Chaining

•We will study various forms of

representation and uncertainty

handling in the next class

period

•Knowledge needs to be

represented

–Production systems of some form

are very common

•If-then rules

•Predicate calculus rules

•Operators

–Other general forms include

semantic networks, frames,

scripts

–Knowledge groups

–Models, cases

–Agents

–Ontologies

A Brief History of AI: 1950s

•Computers were thought of as an electronic brains

• Term “Artificial Intelligence” coined by John McCarthy

–John McCarthy also created Lisp in the late 1950s

•Alan Turing defines intelligence as passing the Imitation

Game (Turing Test)

•AI research largely revolves around toy domains

– Computers of the era didn’t have enough power or memory to

solve useful problems

–Problems being researched include

•games (e.g., checkers)

•primitive machine translation

•blocks world (planning and natural language understanding within the

toy domain)

•early neural networks researched: the perceptron

•automated theorem proving and mathematics problem solving

The 1960s

•AI attempts to move beyond toy domains

•Syntactic knowledge alone does not work, domain

knowledge required

–Early machine translation could translate English to Russian

(“the spirit is willing but the flesh is weak” becomes “the

vodka is good but the meat is spoiled”)

•Earliest expert system created: Dendral

•Perceptron research comes to a grinding halt when it is

proved that a perceptron cannot learn the XOR operator

•US sponsored research into AI targets specific areas –

not including machine translation

•Weizenbaum creates Eliza to demonstrate the futility of

1970s

•AI researchers address real-world problems and solutions through

expert (knowledge-based) systems

–Medical diagnosis

–Speech recognition

–Planning

–Design

•Uncertainty handling implemented

–Fuzzy logic

–Certainty factors

–Bayesian probabilities

•AI begins to get noticed due to these successes

–AI research increased

–AI labs sprouting up everywhere

–AI shells (tools) created

–AI machines available for Lisp programming

•Criticism: AI systems are too brittle, AI systems take too much

time and effort to create, AI systems do not learn

1980s: AI Winter

•Funding dries up leading to the AI Winter

–Too many expectations were not met

–Expert systems took too long to develop, too much money to

invest, the results did not pay off

•Neural Networks to the rescue!

–Expert systems took programming, and took dozens of man-

years of efforts to develop, but if we could get the computer

to learn how to solve the problem…

–Multi-layered back-propagation networks got around the

problems of perceptrons

–Neural network research heavily funded because it promised

to solve the problems that symbolic AI could not

•By 1990, funding for neural network research was

slowly disappearing as well

–Neural networks had their own problems and largely could

not solve a majority of the AI problems being investigated

–Panic! How can AI continue without funding?

1990s: ALife

•The dumbest smart thing you can do is staying alive

–We start over –lets not create intelligence, lets just create

“life” and slowly build towards intelligence

•Alife is the lower bound of AI

–Alife includes

•evolutionary learning techniques (genetic algorithms)

•artificial neural networks for additional forms of learning

•perception and motor control

•adaptive systems

•modeling the environment

• Let’s disguise AI as something new, maybe we’ll get

some funding that way!

–Problems: genetic algorithms are useful in solving some

optimization problems and some search-based problems, but

not very useful for expert problems

–perceptual problems are among the most difficult being

solved, very slow progress

Today: The New (Old) AI

•Look around, who is doing AI research?

• By their own admission, AI researchers are not doing “AI”, they

are doing

–Intelligent agents, multi-agent systems/collaboration

–Ontologies

–Machine learning and data mining

–Adaptive and perceptual systems

–Robotics, path planning

–Search engines, filtering, recommendation systems

•Areas of current research interest:

–NLU/Information Retrieval, Speech Recognition

–Planning/Design, Diagnosis/Interpretation

–Sensor Interpretation, Perception, Visual Understanding

–Robotics

•Approaches

–Knowledge-based

–Ontologies

–Probabilistic (HMM, Bayesian Nets)

–Neural Networks, Fuzzy Logic, Genetic Algorithms